Large language models (LLMs) are increasingly deployed in edge computing environments to reduce latency and preserve privacy. However, their inference process presents fundamental challenges for resource-constrained IoT devices. LLM inference involves computationally asymmetric stages: parallelizable prompt processing and sequential token decoding. This asymmetry creates deployment bottlenecks where IoT devices lack capacity for prompt processing while edge nodes suffer from inefficient sequential decoding. This paper presentsE$^{2}$LLM, an efficient distributed inference framework for large language models in heterogeneous edge-IoT environments.E$^{2}$LLMleverages high-capacity edge devices for structural planning and introduces auxiliary lightweight models to generate segment-specific key-value (KV) caches. These minimal inference artifacts enable collaborative parallel decoding across IoT devices without requiring full model instantiation. The framework employs static-dynamic KV cache separation to minimize communication overhead while maintaining semantic coherence through structure-guided coordination. Extensive evaluation on realistic edge testbeds demonstrates significant performance improvements. Under diverse deployment settings,E$^{2}$LLMachieves 74%–87.7% end-to-end latency reduction compared with several state-of-the-art baselines, while maintaining comparable generation quality; meanwhile, it also delivers a 34.6%–72.2% reduction in communication overhead, improves 9-12 × in energy efficiency. The framework exhibits strong scalability under bandwidth-limited conditions, enabling efficient LLM deployment across heterogeneous edge-IoT environments.

Nowadays IoT devices in Mobile Edge Computing (MEC) networks have been deployed in large-scale quantities to guarantee sensing data collection for anomalous event detection as full as possible even if some devices are in fault. Some techniques, such as clustering and dimensionality reduction, are adopted to eliminate redundant sensing data collection in this large-scale deployment. However, they not only have high computational complexity and easily cause the loss of information on the primary sensing attributes for detection, but also bring certain errors to the detection because of their low sensitivity to data processed. In addition, insufficient collection of primary attribute data samples often results from physical or human factors, and blind imputation of large-scale data gaps without basis may lead to greater irreparable losses. To address the above challenges, we first complete the selection of optimal primary attribute device collection and aggregation (PADCA) path based on minimum spanning tree, reducing data communication cost for redundant primary attributes collection. Then, we propose an anomalous impact correlation search strategy to quickly locate all MEC servers whose management regions have cascading anomalous event and help determine the transferable source MEC servers. Leveraging this, we use transfer learning to help detect anomalous events in the management regions of the MEC servers with insufficient primary attribute data samples, where a particle swarm optimization based back-propagation (PSO-BP) neural network model is used to infer the fusion weight of each primary attribute. Experimental results show that our method achieves higher detection performance in terms of detection time, energy consumption, accuracy, and receiver operating characteristic (ROC) curve compared to the benchmarks by at least 24%, 34%, 0.5 and 0.05.

With the rapid development of 6 G communication technology and the Internet of Things (IoT), mobile edge computing (MEC) is regarded as an effective paradigm of providing low-delay, high-quality services to mobile users. In the IoT device-edge-cloud network, the optimal deployment of MEC servers is a prerequisite for a better task offloading, while the improved performance of mobile users task offloading also indicates the deployment scheme is optimal. Most of current MEC servers deployment studies focus on reducing delay and deployment costs, but ignore the offloading requirements of mobile users with similar task type and cooperative relationship arriving at the same community. In this paper, we study the MEC servers deployment driven by the task offloading requirements of community mobile users in current period by utilizing the stability of their social cooperative relationships to maximize the service satisfaction of all community mobile users in the future task offloading. First, the cooperative relationship strength between mobile users is measured to form a group of resource requesters based on interaction probability, movement trajectory and credit strength. Then, we implement the optimal search of base stations (BSs) using spatial index, followed by the one-to-many matching theory between BSs and community group resource requesters, to balance the load of BSs and reduce the communication delay between them. Finally, we use TD($\lambda$) algorithm and task similarity between cooperative users to deploy MEC servers with suitable resources around BSs so that the deployment scheme can significantly improve the future task offloading performance of all community mobile users. Based on the real data set provided by Shanghai Telecom, it is confirmed that the proposed scheme has significant advantages in improving all community mobile users service satisfaction, with an average improvement of 18.49% compared with the baselines.

Edge service monitoring is essential for ensuring the robustness and efficiency of service executions, where predictive monitoring enables proactive detection of potential service violations. Current approaches for predictive monitoring, which mostly adoptSignalTemporalLogic (STL) specifications for requirements representation and evaluation, primarily focus on deterministic signals, and thus, may lack probabilistic guarantees for uncertainty interpretation. To address these challenges, this paper proposesBayesianSTL(BSTL), an extension ofSTLthat enables probabilistic reasoning over stochastic signals. Specifically,BayesianNeuralNetworks (BNNs) are employed to generate sequences of posterior probability distributions, offering more comprehensive predictive insights compared to traditional point- or interval-based methods with deterministic sequential predictions. Uncertainty interpretation over these distribution predictions is achieved by a novel expected robustness metric that jointly quantifies both the degree and probability of service satisfaction. Thereafter, aBSTL-based predictive monitoring framework is developed, where a service constraint is formally specified by aBSTLformula and interpreted with both qualitative and quantitative semantics. Besides, confidence levels and constraint thresholds ensuring robust satisfaction of aBSTLformula are rigorously estimated. Extensive experiments on publicly available datasets demonstrate thatBSTLoutperforms baseline techniques in terms of expressiveness, robustness, and applicability.

The heterogeneous resources co-optimization in edge networks is essential to enhance the network throughput. Existing load-sensitive (re-)scheduling approaches mostly formulate the heterogeneous resources balancing as a single-objective optimization issue, omitting the balanced usage of heterogeneous resources on a given edge node. Moreover, these approaches are inadequate for the heterogeneous resources adaptive cooptimization, microservice dependency modeling at a more granular level, and multi-step online re-scheduling. Thus, a Dependency-aware Online Microservice re-Scheduling (DOMS) approach is introduced. In particular, we formulate the microservice re-scheduling as a multiple knapsack optimization issue, and solve it through the Double Dueling Deep Q-Network (D3QN) with prioritized experience replay. Our DOMS incorporates a heterogeneous resources adaptive balancing detection algorithm to enable adaptive co-optimization of heterogeneous resources. A fine-grained dependency graph of microservice performance metrics is built, upon which a multi-step scheduling partition algorithm is devised to facilitate multi-step online re-scheduling. Extensive experiments on a public dataset show that DOMS outperforms comparison approaches in terms of latency, energy consumption, balance degree, and throughput.

Edge service monitoring is crucial for ensuring the robustness and reliability of service executions. Predictive monitoring, in particular, enables proactive detection of potential service violations. Existing predictive monitoring approaches, often leveraging Signal Temporal Logic (STL) for requirement specification, primarily focus on deterministic signals, and thus, lack probabilistic guarantees for uncertainty interpretation. To address these challenges, this paper introduces Bayesian STL (BSTL), an extension of STL that enables probabilistic reasoning over stochastic signals. Specifically, Bayesian Neural Networks (BNNs) are utilized to transform deterministic sequential predictions into sequences of posterior probability distributions. Uncertainty interpretation over these distribution predictions is achieved by a novel expected robustness metric that jointly quantifies both the degree and probability of service satisfaction. Thereafter, a BSTL-based predictive monitoring framework is developed, wherein service constraints are formally specified by BSTL formulae and interpreted with both qualitative and quantitative semantics. Besides, confidence levels and constraint thresholds ensuring robust satisfaction of BSTL formulae are rigorously estimated. Extensive experiments on publicly available datasets demonstrate that BSTL outperforms baseline techniques in expressiveness, robustness, and applicability.

Detection of anomalous event at the edge of network has attracted wide attention from both academic and industrial fields recently. During the detection process, several primary sensing attributes are jointly utilized to determine whether an anomalous event occurs or not. However, as the primary attributes of some Internet of Things (IoT) devices are easy missing due to the natural wear and they cannot be timely and accurately accessed, the event detection efficiency is very low. In view of this, our work introduces a digital twin (DT)-assisted detection technology for anomaly identification in a device-edge–cloud architecture. Specifically, for an edge server with missing primary attributes, the probability of anomalous event occurring on it can be calculated by analyzing the primary attribute fusion values of its adjacent edge servers. As a result, it is unnecessary to carry on detection in advance on the edge servers with a low anomaly occurring probability, efficiently reducing the detection cost. For the remaining edge servers with a high probability, the primary attributes with high accuracy are migrated by considering the difference on the historical value variant trend and the fusion effect. Based on this, a decision tree will be built in the integrated DT model for anomalous event detection in advance. Further, the cloud collects other relevant attributes to build a random forest for the final identification and judgment of anomalous events. Experimental results show that our method achieves a higher detection performance in terms of energy consumption, detection time, and accuracy by at least 37.1%, 39.5%, and 1.82% compared to the baselines.

The Web 3.0 and metaverse can empower intelligent application of Connected Autonomous Vehicles (CAVs). The adoption of edge computing can contribute to the low latency interaction between CAVs and the metaverse. Microservices are widely deployed on edge networks and the cloud nowadays. User’s requests from CAVs are typically fulfilled through the composition of microservices, which may be hosted by contiguous edge nodes. Requests may differ on their required resources at runtime. Consequently, when requests are continuously injected into edge networks, the usage of heterogenous resources, including CPU, memory, and network bandwidth, may not be the same, or differ significantly, on certain edge nodes. This happens especially when burst requests are injected into the network to be satisfied concurrently. Therefore, the usage of heterogenous resources provided by edge nodes should be co-optimized through re-scheduling microservices. To address this challenge, this article proposes a Web 3.0-enabled M icroservice R e- S cheduling approach (called MRS ), which is a migration-based mechanism integrating a placement strategy. Specifically, we formulate the MRS task as a multi-objective and multi-constraint optimization problem, which can be solved through a penalty signal-integrated framework and an improved pointer network. Extensive experiments are conducted on two real-world datasets. Evaluation results show that our MRS performs better than the counterparts with improvements of at least 7.7%, 2.4%, and 2.2% in terms of network throughput, latency, and energy consumption, respectively.

Collaborative sensing has emerged as a novel sensing paradigm, entailing multi-sensor data sharing and multimodal modeling to collaboratively understand sensing behaviors. However, current solutions, i.e., data-level and decision-level fusion methods, fall short of generality, expert knowledge, and holistic/chronic perspective. In this paper, we proposeLLMCoSento revisit collaborative sensing with Large Language Models (LLMs). Specifically,LLM-CoSendesigns a semantic-level fusion approach for inference results for collaborative sensing. Such an approach is characterized by its generality, making it applicable to any heterogeneous devices, and its expert knowledge incorporation, which provides chronic, holistic, and insightful perspectives on the inference results. Regarding inference absence challenges, we propose a personalized model design method to constrain inference time, and a voting-based two-pass prompt engineering strategy for token completion. Regarding inference error challenges, we propose an accuracy restoration strategy for personalized models, and a two-level error estimator coupled with self-correction. Experimental results of human digital system use case on four corresponding benchmark datasets showLLM-CoSencan decrease inference absence by 72.83% and inference errors by 7.65% on average.

Mobile Edge Computing (MEC) and Device-toDevice (D2D) peer offloading are two promising paradigms in the mobile Internet of Things (IoT). In this paper, we study the collaborative task offloading with redundant data and codes in large-scale IoT networks, where computing resource-starved IoT devices can offload their tasks to MEC servers via cellular links or to nearby peer devices (PDs) with idle resources through D2D links for execution. IoT tasks usually consist of a series of dependent and parallel subtasks, and the difficulties in current research are (i) how to eliminate redundancy in data or codes between subtasks, and (ii) how to leverage previous experience to adaptively search a set of collaborative MEC servers and PDs for matching offloading of dependent and parallel subtasks. From this, we propose a redundancy-aware adaptive search offloading (RASO) method based on the deep Q-network (DQN). Specifically, we first design a fine-grained task recombination scheme by judging the consistency of subtask data and codes. After that, we organize the global devices into a spatial index MP-tree to reduce the search solution space, and propose a fast adaptive search method based on the DQN combined with MP-tree, where optimal path-guiding parameters training of inner and outer layers is involved to efficiently help achieve collaborative devices to complete specific tasks with the same type. After finding the collaborative MEC servers and PDs along MP-tree for a certain task, a centralized stable matching algorithm is further developed to give a decision of offloading each of its divided dependent and parallel subtasks to the matched one, thereby optimizing offloading delay and energy consumption. Extensive simulation results show that compared to other counterpart solutions, our proposed method has improved task offloading performance in terms of delay and energy consumption.

Anomaly detection has emerged as one of the core research topics to support workflow applications across various domains. To differentiate anomalies from underlying normal patterns of workflows, Graph Neural Networks (GNNs) models have been introduced. These models leverage time series data to construct graph structures, in order to explicitly capture task dependencies among industrial Internet of Things (IoT) devices, and thus, to identify deviations from predicted behaviours as anomalies. However, existing forecasting-based anomaly detection methods may not accurately detect certain anomalies, since they have seldom considered valuable information uncovered by historical sensory data, but presented as domain knowledge. To address this limitation, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Based on which, a knowledge-enhanced graph attention-based forecasting network is developed to predict the future behaviours of IoT devices. Anomalies, such as those caused by cyber-attacks in workflows, are detected by analyzing deviations from these predicted behaviours in conjunction with domain-specific knowledge. A case study is presented, along with extensive experiments conducted on publicly available datasets. Evaluation results demonstrate that KeAD outperforms the state-of-the-art techniques in terms of anomaly detection accuracy.

The proliferation ofInternetofThings (IoT) applications prompts extraordinary demands for the collaboration of large amounts of computational resources provided byIoTdevices in edge networks, and these applications are mostly delay-sensitive. Generally, these resources are encapsulated asIoTservices. Thereafter,IoTapplications can be performed, such that the collaboration of their sub-tasks is achieved through the composition of functionally complementary and geographically contiguousIoTservices. The status of computational resources inIoTdevices may change continuously along with their occupancy and release byIoTservices. Considering the resource-scarceness ofIoTdevices, when the workload ofIoTdevices increases due to more services to be processed, certainIoTdevices may hardly have enough remaining resources to co-host more instances of certainIoTservices prescribed by forthcomingIoTapplications with strict constraints. As a result, the delay satisfaction of both on-running and forthcomingIoTapplications may be negatively impacted, or even hardly be satisfied any longer. To solve this issue, this paper proposes a rEsource-Efficient serviceConfiguration ($E^{2}$rC) mechanism, which aims to optimize the configuration of computational resources provided byIoTdevices with respect to complex requirements prescribed byIoTapplications, through service migration techniques. This service migration problem is formulated as markov multi-phases decisions, which is solved through our enhancedDeepReinforcementLearning (DRL) approach with a two-layerQ-network. Extensive experiments have been conducted upon the dataset of our testbed system. Evaluation results show that our$E^{2}$rCis more efficient than the state-of-art counterparts in satisfying delay constraints ofIoTapplications, while reducing the energy consumption and improving the resource utilization efficiency ofIoTdevices.

The usage of heterogeneous resources provisioned by edge nodes can be co-optimized through re-scheduling microservices. Current (re-)scheduling approaches typically treat the task of co-optimization as a single-objective optimization problem, which cannot address the issue of imbalanced usage of heterogeneous resources (e.g., CPU, memory, bandwidth) on a single edge node. More importantly, these approaches are inadequate in handling: (i) the adaptive co-optimization of heterogeneous resources, (ii) the fine-grained construction of micro service dependencies, and (iii) multi-step online mi croservice re-scheduling. To address these challenges, this paper proposes a Dependency-aware Online Microservice re-Scheduling (DOMS) approach. DOMS formulates microservice re-scheduling as a multi-knapsack optimization problem and solves it using a Double Dueling Deep Q-Network (D3QN) with prioritized experience replay. Specifically, an adaptive heterogeneous resources balancing detection algorithm is developed, incorporating a dynamic detection threshold mechanism. A fine-grained microservice performance metrics dependency graph is constructed by capturing causal relationships to represent sequential execution dependency. Based on this graph, a microservice multi-step scheduling partition algorithm is devised. Extensive experiments are conducted upon publicly-available datasets, and evaluation results demonstrate that DOMS outperforms the state-of-the-art techniques with improvements of at least 1.85%, 6.45%, 0.56%, and 3.18% in terms of latency, energy consumption, balance degree, and throughput. These results highlight the effectiveness and superiority of DOMS in maintaining a balanced usage of heterogeneous resources and improving network throughput, while satisfying latency and energy consumption constraints.

Anomaly detection is a long-standing research topic to support the prompt remedy of potential risks for dependency-aware tasks, where Graph Neural Networks (GNNs) models have been adopted to differentiate anomalies from normal patterns. Generally, GNN models utilize time series data to construct graph structures for capturing task dependencies between Internet of Things (IoT) devices, such that deviations from predicted behaviours are assumed as anomalies. Current forecasting-based anomaly detection methods can hardly detect anomalies, which are uncovered by historical sensory data, but are explicitly specified by domain knowledge. To solve this issue, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Thereafter, a knowledge-enhanced graph attention-based forecasting network is developed to predict future behaviours of IoT devices. Anomalies are detected by analyzing deviations from these predicted behaviours, taking domain-specific knowledge into account. Extensive experiments are conducted based on publicly-available datasets, and evaluation results demonstrate that our KeAD outperform the state-of-the-art techniques in terms of the accuracy of anomaly detection.

Empowered by edge computing, resources and computation capabilities provided by edge devices can be encapsulated as containerized services. When burst requests are coming, there may have edge devices which are overloaded, since most requests are spatially and temporally constrained, and edge devices are resource-scarceness and capacity-limited. Overloaded devices should be relieved through optimally migrating one or more activated services to contiguous edge devices. Besides, sensory data gathered by original edge devices should be periodically transmitted to migrated devices for data analysis purpose. To mitigate this issue, this paper proposes an Energy-efficient Online Service Migration (EOSM) mechanism to conduct the migration of multiple services simultaneously. Extensive experimental results show that our EOSM mechanism outperforms the state of arts techniques in mitigating overloaded services in terms of access latency, energy consumption, and request success rate.

Empowered by edge computing, resources and computation capabilities provided by edge devices can be encapsulated as containerized services, and domain applications can be achieved through service compositions. When burst requests are coming to be satisfied, there may exist edge devices which are overloaded, since requests are mostly spatially and temporally constrained, and edge devices are resource-scarceness and capacity-limited. In this setting, overloaded devices should be relieved through optimally migrating one or more activated services to contiguous edge devices. Besides, sensory data gathered by original edge devices should be periodically transmitted to migrated devices for data analysis purpose. To mitigate this issue, this paper proposes an Energy-efficient Online Service Migration (EOSM) mechanism to conduct the migration of multiple services simultaneously. Specifically, a light service sharing strategy is developed to only transmit the top container layer, and a modified NSGA-II algorithm is adopted to generate one or multiple paths for the container layer and time-series sensory data migration of each migrated service. Extensive experimental results show that our EOSM strategy outperforms the state of arts techniques in mitigating overloading devices in terms of access latency, energy consumption, and request success rate.

Mobile edge computing provides relatively rich computation resources for Internet-of-Things (IoT) task offloading at the edge of networks. As time goes on, user tasks present diverse requirements in function, type, dependency, urgency, etc., which makes edge servers take on dynamically diversified service features to adapt to the requirements of user tasks. Moreover, cache has been studied a lot in recent years for reducing the execution cost of related or dependent tasks. However, jointly considering which result data required to be cached and where to cache is still an intractable problem in task offloading due to dynamically diversified and sensitive features of task and edge servers for prediction. To provide more comprehensive consideration, we propose a multiple features matching scheme, coupled with federated learning-assisted collaborative caching, to enhance the efficiency of task offloading. Specifically, we first build a common features of historical tasks based FI-tree to help search for an edge server that best matches the requested task features. This helps to obtain optimal task allocation and improve offloading performance. Further, the results of tasks related to or dependent on cached results can be obtained directly through the collaborative edge cache prediction model trained by two-stage federated learning. In this way, the amount of data executed for offloaded tasks is reduced, thereby speeding up the return of final results as well as reducing the delay and energy of task execution. Meanwhile, it avoids massive transmission of task results correlated data and also protects the privacy of these data when training the prediction model. Experimental results show that our proposed method outperforms the benchmark approaches through reducing the time delay and energy consumption by at least 15.6% and 18.2%.

Edge service monitoring is essential to guarantee the healthy of service compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, but they are inadequate for that of inter- and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to the execution context. To address these challenges, this paper formulates the problem of edge service monitoring as the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed Compositional Signal Temporal Logic (CSTL) with extended compositional modalities and online parameter settings, an adaptive monitoring mechanism is developed, where constraints are converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results show that CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.

Anomaly detection is an important topic in the Internet of Things (IoT). Recently, some anomaly detection methods based on graph neural networks (GNNs) have gained much attention. However, such methods require a large amount of sensory data for inference, which leads to high energy consumption for data transmission and can hardly be applied in IoT scenarios. This paper proposes a subgraph-based anomaly detection strategy as an energy-efficient anomaly detection method. To accomplish this task, we use graph structure generation to divide subgraphs by feature similarity and reduce energy consumption for data transmission. To validate the effectiveness of our mechanism, we use real-world IoT multivariate time-series data for modelling. The results show that our scheme is more energy efficient and has higher precision compared to other methods in anomaly detection.

With the wide-adoption of edge computing, the functionalities of Internet of Things (IoT) devices can be encapsulated as edge services, to facilitate domain applications through edge service compositions. Considering the capacity-fluctuating and resource-varying of IoT devices, edge service monitoring is essential to guarantee the healthy of their compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, which, however, are inadequate for that of inter-and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to execution context. To address these challenges, this paper proposes a novel logic-based adaptive monitoring mechanism, to achieve the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed Compositional Signal Temporal Logic (CSTL) with extended compositional modalities and online parameter settings, constraints can be converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results demonstrate that our CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.

Summary Federated edge cloud (FEC) is an edge computing environment where servers in the same edge management domain could collaborate to handle latency‐sensitive services, thus better guaranteeing users' requirements on multiple quality of service (MQoS). Traditional scheduling methods only consider whether the server meets the resource requirements of the service, without paying attention to whether their resource characteristics match. In scenarios where server's resources are dynamically changing, this may reduce the resource utilization and the efficiency of service execution. To address this challenge, a dynamic resource adaptation‐multiple quality of service (DRA‐MQoS) algorithm is proposed for service scheduling in this environment. DRA‐MQoS could dynamically evaluate the resource characteristics of servers and services from the perspectives of “individual” and “overall” by combining the historical scheduling data of services and the utilization of different resources of server clusters. By scheduling the services to servers with the same resource characteristics for execution, the proposed policy fusion algorithm efficiently responds to the dynamically changing quality of service (QoS) demands of users by changing the weight parameters of policies. Simulation results in CloudSimSDN show that the energy consumption and execution time of DRA‐MQoS are reduced by 23% and 12%, respectively, compared with existing methods.

With the applicability of edge intelligence in various domains, anomaly detection, which aims to identify unusual and infrequent circumstances, is regarded as a regularly performed task to guarantee the health of the Internet of Things (IoT) applications. Generally, sensory data are gathered at the network edge and completely transmitted to the cloud, where computational-heavy algorithms are mostly adopted to determine the locations of anomaly. Considering the occurrence infrequency of anomalies, this strategy may transmit relatively huge volume of sensory data, which may reflect a healthy situation indeed, to the cloud. To mitigate this problem, this article proposes an accurate anomaly detection mechanism with energy efficiency in three-tier IoT–edge–cloud collaborative networks. Specifically, after gathering sensory data provided by IoT nodes in certain edge networks, the edge node applies the marching squares algorithm to generate isopleths, where an isopleth may capture the boundary of anomaly. A sensory data filtering mechanism is conducted at the edge tier, such that anomaly-relevant sensory data are transmitted to the cloud and, thus, the network traffic is decreased significantly. Thereafter, the boundary of anomaly is obtained, and the locations of candidate boundary nodes are determined by adopting the Kriging spatial interpolation algorithm at the cloud tier. These locations are traversed by mobile sensing nodes at edge networks, and their sensory data are gathered for boundary refinement. Extensive experiments are conducted on an air quality hazardous gas data set from Toward Data Science, and evaluation results show that our technique outperforms the state-of-the-art counterparts in boundary accuracy and energy consumption.

The Internet of Things (IoT) paradigm has established an effective platform to promote the collaboration of resource-limited and duty-cycle IoT nodes, in order to support relative complex service requests that can hardly be achieved by any single IoT node. The functionalities of IoT nodes are typically encapsulated into IoT services, and the satisfaction of service requests is implemented as IoT service composition. Generally, IoT nodes work in turn in terms of their prespecific working cycles, and IoT network topology is constantly varied due to their active/sleeping behavior switching, causing unscheduled response latency. IoT service composition instantiation should be dynamically adjusted and partially redeployed on-demand for supporting request processing efficiently. To remedy this issue, this article proposes a migration-oriented service redeployment (MSrD) mechanism by migrating certain IoT services from their hosted IoT nodes to neighboring ones, in order to support functionally continuous availability. We formulate this problem as a game-theoretic approach, which is reduced to a potential game, where a Nash equilibrium solution is searched for optimizing this service redeployment game. Extensive experiments are conducted, and numerical results show that our MSrD mechanism is promising, compared with the state-of-art techniques, in achieving service redeployment optimization with efficient energy consumption and timely response latency simultaneously.

Anomaly detection is playing an increasingly important role in Internet of Things applications since anomalous events may cause some damage to the physical–social environment monitored by different kinds of smart object devices. In some cases, the occurrence of an anomalous event is caused by the fusion impact derived from several primary monitoring factors. Considering this, we propose a novel anomaly detection mechanism for the events with multiple decisive primary attributes in a collaborative device–edge–cloud architecture, in which a propagation and influence-based correlation is further explored in the edge layer for improving the detection efficiency. During the detection process, multiple primary attributes first cooperate to detect an anomaly in the edge layer in advance. If an anomaly occurs in the subregion managed by an edge device, social-aware interaction relationships between edge devices are further integrated to give a guidance on the detection of correlative anomaly in neighbor subregions. The cloud further analyzes the primary attributes information and the interaction relationship to determine the secondary attributes that are helpful in identifying the final anomaly. A large number of experiments show that our method is superior to the alternative methods in terms of energy consumption, detection time, and accuracy.

The Internet of Things (IoT) has been widely deployed to support versatile applications, where an application can be satisfied by functionally compatible and non-functionally satisfiableIoTservices. Considering the fact that the capacities ofIoTdevices may change dynamically, whether or not, and to what extent, certain constraints can be satisfied during their execution, are to be explored. This observation motivates us to formalize the interpretation of qualitative and quantitative satisfaction for prescribed constraints, and thus, to achieveIoTservice monitoring at runtime. Specifically, we formulate the problem ofIoTservice monitoring as a constraint satisfaction problem, where multiple constraints, including spatial-temporal constraints, energy limitation, and capacity restrictions, are considered. Specification-based monitoring is developed based onSignalTemporalLogic (STL), where a novel accumulativerobustnessmetric is proposed, denotedAccumulativeSTL(ASTL), to emphasize the robust satisfaction over the entire time domain. Thereafter,IoTservice monitoring is converted toASTLformulae, and its constraint satisfaction is interpreted with qualitative and quantitative semantics at runtime. Case studies and extensive evaluations are conducted upon publicly-available datasets, where various influential factors are considered. Experimental results show that ourASTLperforms better than the state-of-the-art's techniques with more robust satisfaction.

With the explosive growth of the Internet of Things (IoT) devices deployed in edge networks, the functionalities of IoT devices are typically encapsulated as IoT services, and user requests can be achieved through the composition of data and/or computation-intensive IoT services. Considering the prediction-uncertainty of forthcoming requests, certain IoT services may (i) not be hosted currently by appropriate IoT devices, or (ii) such an IoT service exists, but its non-functional properties may hardly be satisfied with respect to certain constraints prescribed by requests. To address this challenge, this paper proposes an efficiency-aware service Migration Scheduling (denoted eMS) mechanism in edge networks, in order to migrate IoT services on-demand, and thus, to optimally settle non-satisfiable constraints. Specifically, IoT services are re-scheduled, such that certain IoT services are migrated from their hosting IoT devices to neighboring ones, while minimizing the energy consumption and average delay caused by this service re-scheduling operation. We formulate this service re-scheduling as a multi-objective and multi-constraint optimization problem, which is solved through integrating the greedy algorithm into the fast non-dominated sorting and crowded-comparison operators as the hybrid genetic algorithm (G-NSGA-II). Based on real-life datasets provided by an oil pipeline monitoring project, extensive experiments are conducted, and evaluation results show that our eMS is promising in reducing the energy consumption and average delay of service re-scheduling in comparison with the state-of-art’s techniques.

Anomaly event coverage is usually related to several attributes, among which the primary attribute dominates at the time of improving detection efficiency. In the case of Internet of Things (IoT) devices with complex social-aware relationships, IoT nodes with primary attributes should cooperate with each other through their social-aware interactions, to detect potential event anomalies and further determine the coverage of such anomalies. Existing research has put a lot of effort into designing IoT detection frameworks to discover anomalous sensor data, rarely caring about the social-aware interactions. This paper targets this important efficiency problem, and develops a novel anomaly detection mechanism in collaborative social-edge-cloud architecture. The focus of it is to first construct a vector space based Aggregation Behavior Comparison Detection Model, and quantify the change of monitoring behavior by defining the clustering threshold of vector space. This can quickly judge whether a local social network is abnormal and speed up the abnormal detection rate. If it is, a Social Behavior Correlation Detection Model is further designed based on the correlation of primary attributes derived from the dominating social-aware interaction behavior captured by (primary) edge nodes. This strategy can help detect specific “abnormal” areas managed by one or more edge devices with higher accuracy. In the process of anomaly detection, we also propose a spatial index tree to store the information of IoT nodes, so as to effectively collect and route the perceived data of IoT nodes for anomaly analysis. Experimental results demonstrate that our anomaly detection method promotes the detection efficiency and accuracy in comparison with the state of art’s techniques.

Edge computing is a popular paradigm in solving the problems of long time delay and high energy consumption in Internet of Things (IoT) network, which can effectively realize the IoT task offloading by collaboration of multiple edge servers. Nevertheless, how to choose the appropriate edge servers for offloading the dependent subtasks is still a big challenge, considering the limited resources and computing power of the edge servers as well as the start and end execution time of each subtask. These factors have a great impact on the execution efficiency of the whole task. At present, most of the research works focus on single-hop or multi-hop task offloading, where the edge servers farther away are not considered in the offloading decision. Such task offloading strategy is not optimal, and difficult to achieve high parallel execution of tasks, resulting in some delay-sensitive tasks not being completed within the specified time. In this paper, a two-stage optimization method is proposed to solve the resource allocation problem between edge servers and tasks. In the first stage, we group tasks according to their priorities, and the group with a higher priority is given the preference to resource allocation, thereby ensuring the timeliness of delay-sensitive tasks. Within the same group, resources are competed according to the game theory, and the total delay of all tasks is optimized. In the second stage, we aim to optimize the energy consumption of each task without increasing its completion time by allocating the computing resources to its subtasks based on their maximum completion time. For group resource allocation, we propose a spatial index tree to store the information of all edge servers for optimal server selection. During the selection process, an online learning based double prediction model is utilized to reduce the energy consumption caused by information transmission. We have evaluated the performance of the experiment on iFogSim simulator, and the experimental results show that our proposed method can achieve better performance in terms of time delay and energy consumption.

With the recent adoption of edge computing,Internet ofThings (IoT) devices collaborate at the network edge to facilitate edge-native applications. In this setting,IoTdevices are typically encapsulated asIoTservices to encode their functionalities, and their collaboration is achieved throughIoTservice composition. Due to the continuous resource occupancy, release, and consumption ofIoTdevices at runtime, a composition, which is functionally compatible and non-functionally optimal at this moment, may not hold in the forthcoming time durations, when certainIoTservices may significantly downgrade in theirQuality-of-Services (QoS). To guarantee the compatibility of compositions withQoSvariations, this article proposes an adaptive composition mechanism leveragingComputationTreeLogic (CTL) specifications. Specifically, we formalize the composition as a temporal task, and convert it toCTLformulae with the abstractions of required functionalities and composite structures. Functional compatibility is formally interpreted byCTLsemantics during the execution of compositions. Besides, we construct aQoSDependencyGraph (QoSDG) to captureQoSvariations, and achieve adaptive composition with dynamicQoSsatisfactions. Extensive experiments are conducted upon publicly-available datasets, and comparison results demonstrate that our technique outperforms the state-of-the-art counterparts in heterogenous scenarios with higherQoSdependencies ranging from 0.3$\%$to 27.8$\%$.

With the service-oriented encapsulation of Internet of Things (IoT) devices, IoT services, which are functionally compatible and non-functionally satisfiable, are composed to support domain applications. The execution of IoT services may last for a relatively long time duration in which their capacities may vary significantly. In this setting, whether or not, and to what extent, certain constraints specified upon certain IoT services can always be satisfied during their execution, are to be explored. This observation motivates us to formalize the interpretation of qualitative and quantitative satisfaction for prescribed constraints, and thus, to conduct IoT service monitoring at runtime. Specifically, the requirement of IoT service monitoring is formulated as a constraint satisfaction problem. Specification-based monitoring is developed leveraging Signal Temporal Logic (STL), where a novel accumulative robustness metric, denoted Accumulative STL (ASTL), is proposed to emphasize the robust satisfaction over the entire time domain. Hence, an ASTL-based mechanism is proposed to support IoT service monitoring, where prescribed constraints are converted to ASTL formulae, and interpreted with qualitative and quantitative semantics at runtime. Case studies and extensive evaluations are conducted upon publicly-available datasets. Experimental results show that ASTL performs better than the state-of-the-art techniques with more robust satisfaction.

The rapid growth of the Internet of Things (IoT) and the increasing number of connected devices have propelled the proliferation of offered applications, causing “applications exploding.” In the context of IoT, filtering and selecting the most relevant applications in a given situation is a challenging task. Thus, developing techniques that can alleviate applications exploding and meet users’ requirements is highly demanded for IoT development. This survey focuses on applications exploding in the IoT and reviews some of the existing techniques, such as intelligent sensing, content distribution network, microservices, and 5G, which help mitigate the effects of applications exploding. Furthermore, the survey discusses how to describe user requirements and offer application services to better match the two. In addition, this survey presents the smart home as an instance of typical IoT applications and explores how adaptive users’ requirements for food ordering can be better met when various food provider applications are available for choice. Finally, partially resolved and unresolved bottlenecks brought by applications exploding are put forward to be further researched by the technical and scientific community.

With the proliferation of I nternet o f T hings (IoT) devices deployed in edge networks, the functionalities of IoT devices are typically encapsulated in terms of IoT services. Their collaboration is mostly achieved through the composition of functionally complementary and geographically contiguous IoT services, to achieve complex requests. Considering the capacity constraints of IoT devices, newly incoming requests may hardly be satisfied partially (or completely), since these devices are implementing subtasks of previous requests at this moment. Therefore, candidate IoT devices may have no enough remaining capacity to co-host subtasks of these new requests concurrently. To solve this problem, this article proposes a novel r esource a llocation and s ervice co-placement (RaSP) algorithm to address latency-aware online service reconfiguration problem. Specifically, IoT services are reconfigured upon IoT devices in an optimal manner, such that certain IoT services corresponding to subtasks in previous requests should be migrated online from their hosting IoT devices to neighboring ones, and constraints of these requests are still satisfiable. These released resources can be adopted to implement subtasks (or IoT services) of newly incoming requests. A prototype is implemented using anEdgeSimsimulator. The experimental results show that our RaSP algorithm performs better than the state of the art’s techniques in satisfying the latency of newly incoming and previous requests simultaneously, and reducing the energy consumption of edge networks.

The edge–cloud collaboration network is promising to support complex requirements with temporal constraints, where a requirement can be achieved through the composition of computation-demanding and delay-sensitive services. In this setting, most services should be optimally configured at the network edge, in order to decrease service response latency and reducing network resource consumption. To address this challenge, this article proposes an optimal service configuration mechanism, where temporal constraints between services are mined from event logs through our temporal interval discovery mechanism. Service configuration is formulated as a constrained multiobjective optimization problem, which is solved by our improved nondominated sorting geneticalgorithm II. Extensive experiments are conducted, and evaluation results demonstrate that our approach can find the close-to-optimal service configuration in comparison with the state-of-the-art techniques in terms of delay sensitivity and energy efficiency, especially when edge nodes can co-host a relatively large number of services.

Edge computing promises to facilitate the collaboration of smart sensors at the network edge, in order to satisfy the delay constraints of certain requests, and decrease the transmission of large-volume sensory data from the edge to the cloud. Generally, the functionalities provided by smart sensors are encapsulated as services, and the satisfaction of certain requests is reduced to the composition of services configured upon smart sensors in edge networks. Considering the dynamics and nonpredictability of incoming requests, an adaptive and online service configuration mechanism is essential, especially when various temporal constraints are prescribed by requests and satisfied by configured services. In this article, we formulate this problem in terms of a continuous-time Markov decision process model based on the state–action–reward mechanism. A temporal-difference learning approach is developed to optimize the service configuration while taking long-term delay sensitivity and energy efficiency into consideration. Extensive experiments are conducted, and evaluation results show that our approach outperforms the state-of-art's techniques for achieving close-to-optimal service configuration, and improving the temporal satisfaction of user requests.

As a new form of manufacturing in the industrial Internet era, cloud manufacturing service ecosystem (CMSE) can meet complex customization needs through a dynamic collaborative network between cloud manufacturing services. The source of cloud manufacturing service is social, and such sociality aggravates the uncertainty and dynamics of CMSE. This poses new challenges to the analysis of CMSE's evolution. The existing model social learning evolution (SLE) model only analyzes manufacturing service ecosystem from the perspective of individuals and lacks research on the organizational structure among individuals. In this article, we propose the improved model (SLE2) from a system perspective, which reconstructed the three layers of the SLE model: the individual layer describes the learning and evolution characteristics of service agents; the organization layer describes the competition and cooperation among service agents; and the social layer describes the value-driven social network operation mode. Finally, the article verifies that the SLE2 model is effective through computational experimental results.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this article proposes a value entropy model that links the operating state of the system with the efficiency of value creation, which helps to clarify the performance of the service ecosystem from the perspective of multi-dimensional integration. In addition, a computational experiment system is established to verify the effectiveness of value entropy model, which stimulates the competitive evolution process of two service ecosystems with different strategies. The result shows that our model can provide new ideas for the analysis of service ecosystem evolution, and can also provide decision support for the optimization of operation strategy.

The collaboration of Internet of Things (IoT) devices is promising nowadays to achieve complex requests in edge networks. In this setting, the functionalities of IoT devices are usually encapsulated as IoT services. A request can be fulfilled by the composition of data- or computation-intensive IoT services, which require to either consume a relatively large amount of sensory data or mandate a heavy computation capacity. Discovering functionally complementary IoT services, while satisfying their pre-specified spatial constraints, is a challenge, since certain IoT services may non-exist with respect to current IoT services deployment situation. To remedy this issue, we propose an energy-aware Data- and Computation-intensive service Migration and Scheduling mechanism (DCMS) to re-schedule certain services from their hosting devices to the ones within the geographical region prescribed by the request. Extensive experiments are conducted and evaluation results show that our DCMS is promising in reducing the energy consumption and average delay, in comparison with the state of the art's techniques.

The collaboration of contiguous Internet of Things (IoT) devices is envisioned to satisfy complex applications which are beyond the capacity of single devices. The functionalities of IoT devices are encapsulated as IoT services, and their collaboration is implemented in terms of IoT service composition. Considering the capacity occupancy, release, and consumption caused by the implementation of IoT services, their composition is challenging in capacity-dynamically fluctuating IoT networks. This paper proposes a dynamic IoT service composition mechanism with inter-service dependencies adopted to capture the dynamic changes of IoT devices, and this change is specified by various Quality-of-Service factors. IoT service composition is formalized under Computation Tree Logic specification with certain composite structures and dynamic dependencies, and this composition is formally achieved by an optimized model checking method. Extensive experiments are conducted on publicly available datasets, and evaluation results show that our technique outperforms the state-of-the-art's approaches in relevant performance metrics.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. As shown in Fig.1 , the value creation of service ecosystem consists of three elements: Input, Output, and Operation. Input means customers’ value demands, which drives the constant evolution of service ecosystem. Output means the value created by the service ecosystem in a certain period of time. Operation means the value creation ability of service ecosystem.

With an increasing number of Geographical Information System (GIS) services publicly available on the Web, the discovery of composite GIS services is promising when novel requirements are to be satisfied. GIS services in the repository like ArcGIS software are organized in a tree hierarchy, where a parent node represents a categorial GIS service with a coarser-granularity than its child GIS services, while leaf nodes correspond to atomic and exercisable GIS services. In this setting, discovering appropriate atomic GIS services is challenging. To remedy this issue, this paper proposes a composite GIS service discovery mechanism. Specifically, for the given requirement, select the parent nodes that take the given input parameters as input and remove their inactivated children. Use remaining children to build the network and repeat the previous operation until finding the services that contain the required output. Then record the semantic similarity degree, calculated by services functional description, in this network. By using the simulated annealing algorithm, a composite GIS services solution will be recommended from this semantic network. Evaluation results demonstrate that our approach could give more significant solution compared with the state-of-the-art techniques.

An energy-efficient anomaly detection is fundamental to maintain a healthy status of domain applications in edge-cloud collaboration networks. Generally, various kinds of multimodal sensory data capture heterogeneous attributes, where a certain attribute, called the primary one, may be more significant in detecting certain anomaly. This observation drives us to propose a novel energy-efficient anomaly detection mechanism, where attributes sensed by multimodal smart things (msts) are categorized as primary and secondary ones according to their relevance with the characteristic of this anomaly. This technique includes two steps: 1) an initial anomaly detection in single edge networks. Edge nodes associated with the primary attribute adopt a lightweight object detection model to initially detect the potential occurrence of this anomaly. Certain edge networks are determined where an anomaly is suspected and 2) an anomaly refinement with multimodal and multiattribute smart things in marginal edge networks. The cloud identifies and issues a specific query request to gather anomaly-aware sensory data from smart things with secondary attributes, for refining the detection accuracy of this anomaly, where an adaptive weighted fusion model is developed to analyze sensory data coupling of msts. The experimental results show that this technique performs better than the state of the art on the reduction of energy consumption and query time.

The edge-cloud collaboration networks have been applied to support delay-sensitive Internet of Things (IoT) applications, where applications are represented in terms of service compositions. In this setting, IoT services should be configured mostly at the network edge, and they are offloaded to the cloud only when the capacity of edge nodes can hardly meet the requirement. To solve this problem, this paper proposes to configure IoT services with temporal constraints discovered from event logs. Service configuration is reduced to a constrained multi-objective optimization problem, which can be solved by an improved non-dominated sorting genetic algorithm II. Experimental results demonstrate the efficiency of this technique in comparison with baseline techniques on delay sensitivity and energy consumption.

IoT search engines have attracted increasing attention from both academia and industry, since they are capable of crawling heterogeneous data sources in highly dynamic environment. To process tens of thousands of spatial-temporal-keyword queries per second, query efficiency and communication cost in IoT search engines become critical issues. To address these challenges, caching mechanisms in collaborative edge-cloud computing architecture, which can implement the caching paradigm in cloud for frequent n-hop neighboring activity regions, is proposed in this paper. Thereafter, frequent query results can be achieved quickly leveraging the spatial-temporal-keyword filtering index of n-hop neighbor regions through modeling keywords relevance and uncertain traveling time. Besides, we adopt STK-tree proposed previously to directly answer non-frequent queries. Extensive experiments on real-life dataset demonstrate that our method outperforms the state-of-the-art's techniques in terms of the reduction of the query time and the number of transmitted messages.

This paper proposes to derive temporal constraints and granularities corresponding to individual activities, collaborative activities and their connecting edges from event logs. Specifically, a timed hierarchical business process model is constructed. Temporal anomalies are measured with time-constrained and granularity-aware bounds according to user's acceptance of deviant executions. Temporal interestingness, as the complement to anomaly detection, is evaluated as the most probable execution times that are partitioned into user-defined granules and ranked by probability. Experimental evaluations upon public event logs demonstrate the effectiveness and applicability of our proposed model for temporal anomaly and interestingness detection in terms of accuracy and recall, in comparison with the state-of-art`s techniques.

A collaborative edge network is emerging to support business process applications which are decomposed into several services in terms of their functionalities. In this work, we consider online resource allocation through adaptively configuring services on appropriate edge devices. The scheduling produce is formulated to an Markov decision process, where a Temporal-Difference learning is adopted to achieve delay reduction and energy efficiency.

The workflow fragment discovery is essential to facilitate the reuse and repurposing of the best-practices evidenced by legacy workflows. A novel scientific experiment may be satisfied by the composition of (i) fragments that correspond to general functionalities, and (ii) other fragments that are personalized somehow, in the domain. We denote these types of fragments as basic and personalized patterns, respectively. Based on this observation, this paper proposes a novel workflow fragments discovery mechanism. Evaluation results demonstrate that this technique is more accurate in discovering personalized workflow fragments than the state of art's techniques.

With the Internet of Things (IoT) becoming the infrastructure to support domain applications, IoT search engines have attracted increasing attention from users, industry, and research community, since they are capable of crawling heterogeneous data sources in a highly dynamic environment. IoT search engines have to be able to process tens of thousands of spatial-time-keyword queries per second, making query throughput a critical issue. To achieve this heavy workload, caching mechanisms in collaborative edge-cloud computing architecture, which can implement the caching paradigm in cloud for frequent n -hop neighbor activity regions, is first proposed in this article. With our design, the frequent query result can be gained quickly from the spatial-time-keyword filtering index of n -hop neighbor regions by modeling keywords relevance and uncertain traveling time. In addition, we use STK-tree proposed previously to directly answer nonfrequent queries. Extensive experiments on real-life and synthetic data sets demonstrate that our proposed method outperforms the state-of-the-art approaches with respect to query time and message number.

Concurrent applications may share certain components which can be conducted once for all, while mandating the satisfaction of their spatial-temporal constraints. A mechanism is proposed in this paper to identify common components, and to integrate and optimize concurrent service requests, where a component corresponds to a snippet of IoT service compositions. Consequently, composing IoT services with respect to concurrent requests can be reduced to a constrained multi-objective optimization problem, which can be solved by heuristic algorithms. Experimental results demonstrate the efficiency of this technique in comparison with the state of art's techniques, especially when the number of IoT nodes and functionality-overlapping are relatively large.

Cloud computing, which is claimed to provide plentiful storage, computational, and other resources, has become a promising platform to support resource-intensive applications. Due to the wide adoption of smart things to support domain applications and considering the delay-sensitivity of certain requests and limited network capacity compared with huge data packets to be transmitted, the quality of experience (QoE) may be hard to be satisfied when requests are solely supported by cloud computing. In this setting, edge computing has become an infrastructure to facilitate request satisfaction at the network edge. This article proposes a mechanism to optimize the collaboration of heterogeneous edge servers with certain QoE constraints. Specifically, concurrent requests, which are usually represented in terms of SQL queries, are rewritten as atomic queries, and these atomic queries are optimally assigned to edge servers through adopting an algorithm inspired by the minimum spanning tree, where QoE factors, including the delay, size of data packets, and number of operators, are considered. Evaluation results indicate that the proposed mechanism can effectively improve the QoE of requests compared with the state-of-the-art's mechanisms.

This paper proposes a multi-request cooperative-integrating mechanism to optimize concurrent multi-applications in service-oriented wireless sensor networks (WSNs). Specifically, a sensor node is encapsulated as one or multiple WSN services, which can be categorized into service classes. A service network is constructed by considering the invocation relationship between service classes. Candidate service class chains are recommended. These service classes chains will be instantiated by available WSN services, which can be reduced to a multi-objective and multi-constraint optimization problem, where the spatial-and temporal-constraints, and energy efficiency of the network, are taken into consideration. This combinational optimization problem is solved by adopting heuristic algorithms. Experimental results show that this technique improves the shareability and energy efficiency for supporting concurrent applications.

This paper presents a three-tier framework to facilitate the composition of Internet-of-Things (IoT) services, where these IoT services represent functionalities provided by heterogenous smart things. Various IoT services are categorized into service classes through the categorization of their functionalities. A service network is constructed by considering the invocation relationship between service classes, and service class chains are generated using traditional Web service composition techniques to satisfy the requirement from the functional perspective only. Considering the factors, including spatial and temporal constraints, energy efficiency, and the functional configurability, IoT service composition can be reduced to a multi-objective optimization problem. Heuristic algorithms, such as genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO), are adopted to search for optimal IoT service compositions. Experimental results show that PSO performs better than GA and ACO in searching for approximately optimal IoT service compositions and reducing the energy consumption of smart things in the network.

The large-scale and distributed characteristic of multiattribute sensors requires the fog computing paradigm to support location-awareness and latency-sensitive monitoring and query in industrial applications. In these settings, supporting the preference top-k query processing in skewness distribution is a challenge. In this paper, we propose to mitigate the problem of processing a large number of continuous multiattribute (i.e., multidimensional) top-k queries, each with its specific preference, in fog-supported wireless sensor networks. Specifically, a priority-aware index tree is constructed to support the efficient filtering through querying branch nodes according to their top-k result generation probabilities. We have also considered three situations to generate the filter thresholds for the preference user queries. To further eliminate the transmission of invalid thresholds and query results, an enhanced top-k query processing mechanism based on dual transform and K-sky band is developed. Experiments using synthetic dataset and Intel Berkeley Lab dataset show that our proposed approach can have significant improvements in energy efficiency over other reactive methods.

This article proposes an approach for identifying and recommending scientific workflows for reuse and repurposing. Specifically, a scientific workflow is represented as a layer hierarchy, which specifies hierarchical relations between this workflow, its sub-workflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows. A graph-skeleton based clustering technique is adopted for grouping layer hierarchies into clusters. Barycenters in each cluster are identified, which refer to core workflows in this cluster, for facilitating cluster identification and workflow ranking and recommendation. Experimental evaluation shows that our technique is efficient and accurate on ranking and recommending appropriate clusters and scientific workflows with respect to specific requirements of scientific experiments.

Hosting multiple applications in a shared infrastructure of wireless sensor networks is a trend nowadays, and sharing sensory data for answering concurrent applications is a promising and energy-efficient strategy. To address this challenge, this paper proposes an energy-efficient query optimization mechanism for supporting multiple concurrent applications leveraging our two-tier cooperative caching mechanism. Specifically, query requests for concurrent applications are represented as binary strings, which are reduced to a single one for avoiding the reprocessing of shared subquery requests. This reduced query request is answered through our cooperative caching mechanism, where sensory data, which are highly possible to be reused for answering forthcoming query requests, are cached at the sink node (SN). Besides, the gray model GM(1, 1) is adopted for forecasting sensory data units which may be interested mostly by forthcoming query requests. These units of sensory data may be prefetched from the network and cached at the SN. Experimental evaluation shows that this approach can reduce the energy consumption significantly, and improve the network capacity to an extent, especially when the number of concurrent query requests is relatively large.

Industrial Internet of Things has been widely used to facilitate disaster monitoring applications, such as liquid leakage and toxic gas detection. Since disasters are usually harmful to the environment, detecting accurate boundary regions for continuous objects in an energy‐efficient and timely fashion is a long‐standing research challenge. This article proposes a novel mechanism for continuous object boundary region detection in a fog computing environment, where sensing holes may exist in the deployed network region. Leveraging sensory data that have been gathered, interpolation algorithms have been applied to estimate sensory data at certain geographical locations, in order to estimate a more accurate boundary line. To examine whether estimated sensory data reflect that fact, mobile sensors are adopted to traverse these locations for gathering their sensory data, and the boundary region is calibrated accordingly. Experimental evaluation shows that this technique can generate a precise object boundary region with certain time constraints, and the network lifetime can be prolonged significantly.

With the advancement in energy harvesting in terms of wireless charging techniques, it provides a novel way to solve traditional energy constraint problems in Wireless Sensor Networks (WSNs). Renewable energy such as solar, wind, and geo-thermal energy is converted to energy-storage and further use via harvest-then-transmit strategy. This article introduces a two-layer sleep scheduling system in energy-harvesting WSNs with an aim to satisfy sustainable throughput by analysis and optimization of network performance. Evaluation results provide a typical demonstration of how to obtain the appropriate value of key parameters according to specific requirement.

The boundary detection of continuous objects has become an important research challenge in Wireless Sensor Networks (WSNs), where improving the accuracy of boundary and reducing the energy consumption are the primary factors to be considered. To address this challenge, this article proposes a tow-stage boundary area detection scheme in duty-cycled WSNs, and sensor nodes are deployed in a dense fashion. Experimental evaluation result shows that the refinement procedure can refine the boundary area, where half of the initial boundary faces area should be reduced in most situations.

With the advancement in energy harvesting, wireless powered communication networks overcome the problem of replacing fixed energy sources, e.g., batteries in difficult access areas of industrial networks. However, the harvested energy is not always enough to support reliable and low end-to-end data transmission in industrial wireless sensor networks (IWSNs). This poster introduces an energy utilization-concerned sleep scheduling in wireless-powered IWSNs with an aim to balance network demand and residual energy.

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. Knowledge graph has become a powerful tool to represent knowledge in the form of a labelled directed graph and to give semantics to textual information. A knowledge graph is a graph constructed by representing each item, entity and user as nodes, and linking those nodes that interact with each other via edges. Knowledge graph has abundant natural semantics and can contain various and more complete information. Its expression mechanism is close to natural language. However, we still lack a unified definition and standard expression form of knowledge graph. We propose to clarify the expression of knowledge graph as a whole. We clarify the architecture of knowledge graph from data, information, knowledge, and wisdom aspects respectively. We also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.

Underwater environments may vary gradually even when the occurrence of events is detected. Sensory data may follow a certain trend and are predictable during certain time durations. Taking these into consideration, a simple but practical data prediction mechanism is adopted for estimating sensory data and the geographical location of sensor nodes at sink nodes, and these data are synchronized with those sensed by underwater sensor nodes only when their variation is beyond a prespecified threshold. Leveraging these predicted data, the coverage and sources of potential events are identified by the sink node, and the evolution of these events is determined accordingly. Evaluation results show the applicability and energy-efficiency of this approach, especially when the variation of network environments follows certain and simple patterns.

This article proposes to identify and recommend scientific workflows to promote their reuse and repurposing. Specifically, a scientific workflow is converted into a layer hierarchy, which specifies hierarchical relations between this workflow, its sub-workflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows in order to construct a scientific workflow network model. A graph-skeleton based clustering method is adopted for grouping layer hierarchies into clusters. Barycenters in clusters are identified for facilitating cluster identification and workflow ranking and recommendation. Experimental result shows that this technique is efficient and accurate on ranking and recommending appropriate clusters and scientific workflows.

This paper proposes to detect event coverage and determines event sources. Generally, an appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph. Event sources are determined which correspond to the barycenters in this graph. Experiments show that this technique is more energy efficient, especially when the network topology is relatively steady.

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE.

Recently, workflow fragments gains increasing momentum for reuse and re-purpose in Cyber-Physical Systems. To the end, this article proposes to detect and recommend workflow fragments gratifying e-Scientist requirement. Specifically, most common workflow fragments in the form of layer hierarchy are extracted from scientific workflows, which are collected in the myExperiment repository. Annotations for those workflow fragments are developed to support the discovery of workflow fragments. Consequently, an approach for workflow fragments rank and recommendation is presented considering the semantics and structure of workflow fragments.

This is the report for the CPS track: Capacity-Driven Processes and Services for Cyber Physical Society.

Wireless sensor networks (WSNs) are integrated as a pillar of collaborative Internet of Things (IoT) technologies for the creation of pervasive smart environments. Generally, IoT end nodes (or WSN sensors) can be mobile or static. In this kind of hybrid WSNs, mobile sinks move to predetermined sink locations to gather data sensed by static sensors. Scheduling mobile sinks energy-efficiently while prolonging the network lifetime is a challenge. To remedy this issue, we propose a three-phase energy-balanced heuristic. Specifically, the network region is first divided into grid cells with the same geographical size. These grid cells are assigned to clusters through an algorithm inspired by the${{k}}$-dimensional tree algorithm, such that the energy consumption of each cluster is similar when gathering data. These clusters are adjusted by (de)allocating grid cells contained in these clusters, while considering the energy consumption of sink movement. Consequently, the energy to be consumed in each cluster is approximately balanced considering the energy consumption of both data gathering and sink movement. Experimental evaluation shows that this technique can generate an optimal grid cell division within a limited time of iterations and prolong the network lifetime.

This paper proposes a novel method for discriminating the supraventricular tachycardias and the ventricular tachycardias via a high dimensional linear discriminant function and a perceptron with a multi-piece domain activation function having multi-level functional values. The algorithm is implemented via the mobile application. First, the discrete cosine transform is applied to each training electrocardiogram. Then, these discrete cosine transform coefficients are scaled down according to their frequency indices. These scaled discrete cosine transform coefficients of each electrocardiogram are employed as features for performing the discrimination. Second, the high order statistic moments of each feature of the training electrocardiograms corresponding to the same type of tachycardias are evaluated. These high order statistic moments of each feature corresponding to same type of tachycardias form a vector. Third, the high dimensional linear discriminant function is employed to minimize the intraclass separation and maximize the interclass separation of these statistic moment vectors. In particular, new vectors are formed by projecting these statistic moment vectors to the high dimensional linear discriminant function. Fourth, the principal component analysis is employed to reduce the dimension of the projected vectors. Finally, a bank of perceptrons with multi-piece domain activation functions having multi-level functional values is employed for performing the discrimination. By using this bank of perceptrons, the condition for general two class pattern recognition problems achieving the error free pattern recognition performance is guaranteed. Computer numerical simulation results show that our proposed method is robust and effective.

This paper focuses on using wearable equipment to make workers participate in constructing monitoring maps in large-scale petrochemical plants, which collaborates with static sensor nodes in given areas. Furthermore, this study provides a sensing pattern with less cost and higher flexibility, which effectively contribute to collective effort with static sensor nodes. Several open research issues are discussed.

Cyber Physical Society (CPS) feature a tight integration between computation, communication, and control in their operation and interactions with the environment in which they are deployed. The concept of capability, which is the semantics of an action or specific functionality, is therefore a cornerstone in modern Cyber Physical Society, especially those service or process aware. In this track, we present dedicated techniques for capability management and engineering, which can be applied and reused in a large number of areas such as SOA, BPM, Cloud Computing and Internet of Things. These techniques will certainly boost several related research efforts in Cyber Physical Society. This track aims at shedding the light on the importance of capability engineering as well as at bringing together researchers and practitioners working in capability engineering to achieve the vision of cyber physical society by means of current techniques.

Nowadays, green energy management is a fundamental perspective for supporting cyber-physical interactions and managing energy resources, and smart grid is emerging as the next generation energy management paradigm. Toward the green energy management and smart grid, there are several open issues to be explored. This special issue is to provide a platform for the last results in the related topics.

In the hybrid wireless sensor networks, mobile sinks move to pre-specified sink locations to gather sensory data provided by static sensors. In this setting, scheduling mobile sinks efficiently while prolonging the lifetime of network is a challenge. To remedy this problem, we propose a three-phase energy-balanced heuristic in this paper. We firstly divide the hybrid WSN into grid cells, cluster these grids into clusters according to data communication consumption, and then rebalance these clusters with considering movement consumption by a heuristics algorithm. Therefore, all clusters are balanced in energy consumption when considering the cost of both data gathering and sinks movement. The evaluation results indicate that our technique can compute an optimal grid division within limited time of iterations.

Cyber Physical Society (CPS) feature a tight integration between computation, communication, and control in their operation and interactions with the environment in which they are deployed. The concept of capability, which is the semantics of an action or specific functionality, is therefore a cornerstone in modern Cyber Physical Society, especially those service or process aware. In this track, we present dedicated techniques for capability management and engineering, which can be applied and reused in a large number of areas such as SOA, BPM, Cloud Computing and Internet of Things. These techniques will certainly boost several related research efforts in Cyber Physical Society. This track aims at shedding the light on the importance of capability engineering as well as at bringing together researchers and practitioners working in capability engineering to achieve the vision of cyber physical society by means of current techniques.

In the Internet of Things, smart things communicate with each other, and sensed data are aggregated and queried for satisfying certain requests of end-users. When a region of interest requires to be monitored continuously, the strategy that each query is to be executed independently through gathering sensed data of target sub-regions may not be energy efficient, since the values of sensors may have no significant difference in proximate sensing time-slot in some applications. To mitigate the energy consumption in this context, in this paper we propose an energy-efficiency hierarchical clustering index tree for organizing these grid cells. Then, we develop a time-correlated region query technique for answering continuous queries. Theoretical analysis show that our technique is energy efficient compared with traditional techniques.

In service-oriented computing, a user usually needs to locate a desired service for: (i) fulfilling her requirements or (ii) replacing a service, which disappears or is unavailable for some reasons, to perform an interaction. With the increasing number of services available within an enterprise and over the Internet, locating a service online may not be appropriate from the performance perspective, especially in large Internet-based service repositories. Instead, services usually need to be clustered according to their similarity. Thereafter, services in one or several clusters are necessary to be examined online during dynamic service discovery. In this paper, we propose to cluster data providing (DP) services using a refined fuzzy C-means algorithm. We consider the composite relation between DP service elements (i.e., input, output, and semantic relation between them) when representing DP services in terms of vectors. A DP service vector is assigned to one or multiple clusters with certain degrees. In addition, we introduce some operations for managing service clusters, when new services emerge or existing services disappear or become unavailable. When grouping similar services into one cluster, while partitioning different services into different clusters, the capability of service search engine is improved significantly. We have prototyped our approach and the source code is freely available on the web. We have evaluated our clustering approach in different settings and the results are very promising.

The application of spatial database is increasingly widespread. How to effectively store and organize multidimensional space data and improve the processing efficiency of multidimensional data has become a central issue. R-tree is one of the most widely used spatial indexes. Due to the existing large overlap and coverage among the nodes of R-tree, the search path of a data object is not unique, and the search efficiency declines sharply when the amount of data increases. Based on the analysis and research of previous index trees, a novel DR-tree index based on the diagonal lines of MBR is proposed in this paper. DR-tree uses the diagonal line of MBR to indicate spatial data objects and construct R-tree, still adopt the endpoint coordinates of MBR diagonal to signify the location of leaf nodes or non-leaf nodes. Since MBR is simplified, the coverage and overlap among regions are also reduced. Experimental results show that the new index tree is superior to R-tree in performance. The query paths of data objects are single, the insertion, deletion, and query efficiency of data objects are significantly improved, and the performance of the new index tree becomes more apparent when the amount of data objects increases.

Domain decomposition method (DDM) and a hybrid MPI/OpenMP programming model both provide powerful strategy for performance computing. For large-scale partial differential equations, this paper presents a hybrid MPI/OpenMP model based on overlapping DDM and carries out numerical experiments. Numerical experiment results demonstrate that the proposed method has a better parallel performance.

More and more location-based services become relying on the logical notion of a physical location, known as logical location (e.g. Starbucks, KFC). In this paper, we propose a new way to identify logical location using (1) a GPS-enabled mobile phone and (2) a wearable camera embedded in user's glasses. When a user with a wearable camera is detected paying attention to a certain physical location, all the logical locations within the error range of the GPS coordinates are considered as the matched candidates. We select the representative frames in the video stream corresponding to user's interested location in real-time and use multi-view images taken beforehand to represent each logical location. We then extract the Scale Invariant Feature Transform visual features from both the representative video frames and pre-stored images of candidate logical locations for video-image matching, the logical location that the user pays attention to can thus be identified. In order to differentiate the cases where users watch certain objects rather than a logical location in the street, we use Support Vector Machine to classify the two cases so that only the valid logical location is identified. Our proposed approach is proved weather and user independent, and it does not request additional user efforts compared with previous solutions. The results tested using a real-world dataset can achieve an average accuracy of 91.08%.

In recent years, we witness the increasing trend that more applications are developed by composing Web services. Services interact with each other in ways not necessarily foreseen during their development phase. In this setting, mismatches usually exist between services, and adapters are typically synthesized to reconcile mismatches occurring in certain interactions. The technique that identifies the most suitable provider service from a set of functionally equivalent candidates with respect to certain requirements specified by the requester is essential. To address this challenge, we propose a technique called adaptability assessment , which 1) provides a set of conditions that determines when service interactions can be conducted and 2) computes an adaptation degree that specifies to what extent a service protocol is adaptable to another service protocol. Adaptability assessment complements the techniques that synthesize adapters. Specifically, when adaptability assessment suggests that two service protocols can conduct some interactions according to the adaptation mechanisms of a certain adapter and these interactions can fulfill the requester's requirements, then the effort of synthesizing an adapter is beneficial to potential service interactions. As such, the requester can acknowledge whether his/her expected interactions can be conducted or not and under which conditions. This is important before conducting an interaction, particularly when this interaction is critical, long running, and nonrepeatable.

In this paper we propose a technique called replaceability assessment where, according to the adaptation mechanisms of a certain adapter, it provides a set of condition pairs that determine when one protocol can be replaced by another, and computes a replacement degree that specifies how replaceable two protocols are. The set of condition pairs and the replacement degree are complementary criteria to be used by the requestor for identifying the most suitable provider service from a set of functionally equivalent candidates.

In a peer-to-peer (P2P) live streaming system, peer requests collide when multiple peers request data pieces from the same peer (or media server). When collisions occur, some of the peers' requests fail and retries have to be taken, which delays peer's receipts of pieces. This paper shows that request collisions occur frequently at both media servers and peers, and have big impact on system performance. It then proposes two algorithms to address this issue. The first is a novel admission control algorithm at the media server. The second is a peer selection algorithm in which peer requests pieces from neighbors with low collision probability. Simulation results show that the proposed algorithms improve system performance significantly.

Abstract Given the inherent autonomy, heterogeneity, and continuous evolution of Web services, mismatches usually exist between service protocols. Adapters are typically used to reconcile these mismatches. Before synthesizing an adapter, a service requestor is often willing to assess whether her expected interactions can be conducted or not. The effort of synthesizing an adapter is beneficial only if the result of this assessment is positive. Previous effort analyzing service interactions focused on either (i) compatibility analysis for checking whether interactions can be conducted in a direct manner or (ii) adapter synthesization for reconciling mismatches. In this paper we propose a new kind of adaptability assessment that (i) determines whether two service protocols of a requestor and a provider are adaptable, (ii) computes an adaptation degree, and (iii) identifies conditions that determine when these two service protocols can be adapted. This adaptability assessment provides complementary criteria to the service requestor for selecting a suitable service protocol from a set of functionally equivalent candidates according to her requirements. Copyright © 2010 John Wiley & Sons, Ltd.

This paper proposes a formal technique to verify whether or not an expected interaction is adaptable. We first present our observation that a mediated service interaction is synchronizable. This fact is a prerequisite of our approach. Hereafter, we formally model a protocol scenario (i.e., a part of

Web service interoperations have triggered a growing interest in analyzing compatibility and similarity for public processes. However, current approaches are limited since they mainly focus on control-flow but largely ignore data-flow. This ignorance causes that they improperly regard public processes, which can interact with or be replaced by each other with the help of process mediators, as incompatible or different. To address these problems, we propose a novel approach to check compatibility and similarity of public processes considering both control-flow and data-flow. We firstly generate scenarios and views to describe a public process. Then the degree of compatibility and similarity of public processes are computed based on pairwise compatibility or similarity of their views. Our approach will benefit service modelers and users not only for a better understanding of public processes, but also for checking compatibility and similarity of public processes, which will further identify and solve mismatches, and thus facilitate Web service interoperations.

Web service interactions lie in the core of SOA. Due to the autonomy, heterogeneity and continuous evolution of Web services, mediators are usually needed to support service interactions to overcome possible mismatches that may exist among business processes. In this paper, we introduce a space-based architecture for process mediator which considers both control-flow and data-flow, present possible mismatch patterns, and suggest how they can be automatically mediated. Our work can be used to perform runtime mediation and thus to facilitate service interactions.

Control and data dependencies are important information in business processes that supports process modeling, analysis, and execution. However, sequencing constraints, which are prescribed by control structures, obfuscate the true sources of dependencies. In addition, most work improperly equalizes sequencing constraint and control dependency, and regards data dependencies as a flow of data processing relying on sequencing constraints.In this paper, business activities are described with a semantic description that defines precondition, effect, input, and output. Based on which we specify what control and data dependencies are. Control dependencies are related to the precondition and the effect. Mandatory data dependencies are related to the input and the output, while optional data dependencies are derived from possible conditions on business activities. All control and data dependencies are optimized into a minimal dependency graph which captures essential dependencies to be preserved. A sequencing constraint is possibly due to control and/or data dependencies. A clear view on the relation and the difference between sequencing constraint and control/data dependency is crucial to better support process modeling, analysis, and execution.

Within the vision of ubiquitous multimedia, using multimedia sensor nodes, such as audio, image, and video sensor nodes, in wireless sensor networks can dramatically enhance the capability of sensor networks for event description. Different kinds of holes can easily appear in sensor networks. For example, tens of sensor nodes can consist of a hole in a small area because of overload. Efficiently and reliably transmit these multimedia streaming data back to the base station while bypassing these static or dynamic holes is one of the key challenge issues. In this paper, we propose a new Two Phase geographical Greedy Forwarding (TPGF) routing algorithm for exploring one or multiple optimized hole-bypassing transmission paths. We demonstrate the execution of TPGF routing algorithm by using our implemented simulator NetTopo. Comparison and simulation results show that our algorithm can effectively solve the identified problems and is better than the famous GPSR routing algorithm in both functionality and performance aspects.